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Author SHA1 Message Date
pragent-bot 78bcf6a9a0 feat(review): multi-lens orchestration — 5 parallel opencode subprocesses (security/docs/code-quality/tests/perf) 
On by default, opt-out via "reviewers": []. 290 tests pass.

- pilot/opencode_review.py: ReviewerSpec dataclass, default_reviewers(),
  parse_reviewers_config(), parse_triage_config(), resolve_reviewers(),
  _normalize_lens_finding(), posthash() (matches feedback.py scheme),
  _agreement_hash() (severity-free for cross-lens promotion), _tone_strip(),
  synthesize() 7-stage (severity_floor → tone-strip → length cap → per-lens
  max → per-file cap → dedup by _posthash → cross-lens severity promote →
  per-PR cap), run_lenses() (ThreadPoolExecutor pool=4), triage(),
  _intersect_with_triage(), _filter_by_skip_if(), run_lenses_review().
  run() routes to fan-out when config.reviewers[] present or PRAGENT_REVIEWERS=1.
- pilot/ai_review.py: parse_repo_config learns reviewers[] and triage objects
  (id regex /^[a-z0-9][a-z0-9-]{0,31}$/, 8-entry cap, agent_file/model/
  severity_floor/max_findings/activation/skip_if_all_changed_paths/hotpath_globs).
  review_pr branches to opencode_review.run_lenses_review when configured.
  _render_collapsible_usage shows lenses: ... line when present.
- .opencode/agents/{docs,code-quality,triage}.md: 3 new lens subagents.
- .opencode/skills/lens-orchestration/SKILL.md: strict-JSON contract every
  lens subagent MUST honor.
- .opencode/agents/pragent.md: slim to coordinator; no more hardcoded
  @security/@tests/@perf delegation; loads lens-orchestration skill.
- .opencode/README.md: rewrite 'Add a review lens' recipe for multi-lens.
- pilot/README-webhook.md: new 'Multi-lens pipeline' section (diagram +
  default roster + config schema + env vars + cross-lens dedup contract).
- tests: 36 new tests (test_ai_review.py +12 reviewers/triage/usage,
  test_opencode_review.py +24 orchestration). posthash golden-vector matches
  feedback.py exactly across 5 severity × 2 line cases.
 2026-08-20 22:16:21 +00:00
Marcos 998f793ec2 feat(agent): tighten prompt to bound beyond-diff reads + de-generalize cost-model labels 
Three changes from operator feedback:

1. Per-comment � attribution restored on inline comments (operator wants
   it back — the PR-level collapsible is collapsed by default, so the
   attribution is the visible signal of per-finding cost share).
   Hidden only when no _tok_attrib was computed (legacy callers / ollama
   path without usage metering).

2. Agent prompt now bounds reads beyond the diff — the single biggest
   driver of input-token bloat on long agent loops:
     * ≤ 5 file reads beyond the diff for the entire review
     * ≤ 80 lines per read (use --offset + --limit)
     * ≤ 3 grep calls beyond the diff (prefer rtk grep)
     * no re-reads of files already seen
     * no directory walks (ls -R, find .)
     * honor .pr-review.json:exclude_paths

3. De-generalize cost_model calibration labels. The OBSERVED_RUNS list
   referred to `gitea_admin/pragent#7` — a real internal repo path that
   blocks commercialization. Replaced with `internal/hardening-PR (16
   files, 1020 insertions / 91 deletions)`. The numbers (input/output
   tokens, steps, duration) are unchanged — only the labels are
   generic.

Tests:
  * test_inline_comment_body_with_attribution_line — asserts 🪙 line
    shows when _tok_attrib is set
  * test_inline_comment_body_no_attribution_no_coin_line — still
    verifies the line is hidden when no attribution data
  * test_observed_report_prices_every_model — asserts no internal
    repo name appears in the rendered report
Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-20 17:35:06 +00:00
Marcos f6be2b3c61 feat(review): PR-level collapsible metadata + emoji-tagged inline comments 
PR-level comment layout (per operator's format guide):
  * Summary of Changes — 2-4 bullets, sourced from the agent's new
    `summary_changes` JSON field. Falls back to splitting the prose
    `summary` if the list is missing.
  * Key Risks & Concerns — bullets from the new `risks` JSON field.
  * Findings Overview — Markdown table covering every finding
    (severity emoji / location / one-line problem). Both anchored and
    unanchored findings appear here so the table is the single scan point.
  * Unanchored Notes — bullets with severity + fix + Markdown-linked ref,
    for findings with no post-change line to anchor.
  * AI Usage & Run Details — wrapped in a <details>/<summary> collapsible
    so the body stays scannable. Cost line stays inside it.

Inline comment shape:
  * Severity badge: 🔴 [HIGH] / 🟡 [MEDIUM] / 🔵 [LOW] / ⚪ [INFO].
    Unknown severities fall back to � [INFO].
  * 1-2 short paragraphs of problem; **Fix:** label for the fix line.
  * Standard ```suggestion fence for replacement code (Gitea/Forgejo
    apply-on-click). Language-tagged fences are no longer used for
    single-file diffs.
  * Reference as a Markdown hyperlink, visible label truncated to
    <=60 chars; the underlying URL is preserved verbatim.
  * NO per-comment 🪙 token attribution. All telemetry stays in the
    collapsible block on the PR-level comment.

Agent prompt updated to emit `summary_changes` and `risks` in the JSON
output (backward-compatible — older outputs missing them still parse;
they fall back to splitting the prose `summary`).

Tests: 15 new (severity emoji mapping, reference truncation, findings
table escaping, collapsible usage rendering, summary_changes+risks
layout). Existing tests updated for the new structure.
Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-20 16:51:20 +00:00
Marcos 5302e8dcd7 fix(review): salvage findings from nested-object fences + bare arrays + unfenced tail JSON 
The canalhandia PR review lost all findings because the agent ran out of
context before emitting the closing json fence. Three failure modes hit
the old regex \{.*?\}:
  * nested objects inside the fence truncated at the first }
  * bare arrays (no {summary, findings} wrapper) returned []
  * unfenced JSON in the prose tail was never reached (first not last)

Replace the regex with a balanced-brace scanner:
  * _last_json_block walks the fence contents with a depth counter so
    nested objects survive
  * _last_balanced_json + _balanced_json_substring handle bare arrays and
    prose-tail JSON when no fence is present
  * _parse_json_tolerant returns list as well as dict; parse_findings and
    parse_review_output accept a bare array as the outer value

Agent prompt tightened: reserve the final step for emitting the JSON
block so the analysis isn't lost when context runs out.

10 new tests in tests/pilot/test_ai_review.py cover the new shapes.
Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-20 16:29:44 +00:00
Marcos 30d2a3d7da feat(factory): five review skills + a per-review cost model 
Skills — the primary now loads conditionally (each one is input tokens), per a
load table in pragent.md:

- attention-tiering: classify every PR trivial/lite/full/oversized BEFORE
  reading anything, and cap file reads, linter runs and subagent fan-out per
  tier. This is the cost governor; the other skills defer to its budget.
- linter-playbook: per-ecosystem detect-and-run commands scoped to changed
  files, the never-install rule, and how to turn a diagnostic into a finding
  instead of pasting tool output.
- security-lens: the inline security checklist for when @security isn't worth
  delegating, built around a source -> sink test each finding must pass.
- malicious-change: hostile-PR detection — injection aimed at the reviewer,
  install/CI-time hooks, obfuscated payloads, dependency confusion, logic
  backdoors. Complements the runtime containment added in the previous commit:
  that stops the agent being hijacked, this makes it report the attempt.
- comment-craft: how to write problem/fix/suggestion so a maintainer can act in
  one read, and what to cut.

pilot/cost_model.py — prices a review against published Claude and OpenAI rates
(fetched 2026-08-18). Prompt sizes are measured from the factory files rather
than guessed; per-tier workloads come from the tiering budgets. The model is
explicit about the thing that actually dominates an agent loop: the whole
conversation is resent every step, so caching moves ~2.3x of the bill.

Blended over a 5/35/55/5 mix with caching on: ~$0.61/PR on Opus 5 or GPT-5.6
Sol, ~$0.24 on Sonnet 5 or Terra, ~$0.12 on Haiku 4.5, ~$0.02 on Luna. At 350
PRs/month that's ~$212 / ~$85 / ~$43 / ~$8.50.

Tests: 101 -> 122.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01B11e8TZZxJyzHW7jj7KWUN
 2026-08-18 04:53:49 +00:00
Marcos 8c491a7626 harden(pilot): contain hostile PR content, bound the webhook, fix anchoring 
The reviewer runs an opencode agent with `bash: "*": allow` over a checkout of
the PR author's branch, and the pod holds a Gitea Write credential. Those two
facts had no wall between them.

Security
- _build_env now allow-lists the subprocess environment instead of inheriting
  it, so PRAGENT_BOT_TOKEN and WEBHOOK_SECRET never reach the agent. This was
  the live hole: a PR body or an AGENTS.md could ask the agent to `curl` the
  token out, and it had both the value and the tool.
- sanitize_workdir deletes author-controlled agent-instruction files from the
  checkout before opencode starts (AGENTS.md at any depth, CLAUDE.md,
  .cursorrules, a repo opencode.json/.opencode, copilot-instructions.md).
  opencode loads nested AGENTS.md as instructions, so a PR could otherwise ship
  its own system prompt. They are still reviewed, as data.
- The brief fences PR title/body and diff in --- UNTRUSTED --- markers under a
  trust-boundary preamble; the pragent agent, the three lens subagents and the
  review-methodology skill now treat injection attempts as a critical finding
  to report rather than an instruction to obey.
- .pr-review.json is read from the PR's base branch, not the head sha. Its
  `instructions` field is spliced into the reviewer's prompt, so head-ref
  reading let any author rewrite the reviewer's rules. Fields are length-capped.
- Untar rejects escaping symlinks, parent traversal, and writes through a
  planted symlink (tar-slip).
- The image runs as uid 10001 instead of root.

Robustness
- Bounded review concurrency (PRAGENT_MAX_CONCURRENT_REVIEWS, default 2). Each
  review forks an opencode process; a thread per delivery was a fork bomb on a
  burst of labels or Gitea retries.
- An in-flight (repo, index, sha) claim closes the check-then-act race in the
  sha-marker dedupe, where two deliveries a second apart both read "not yet
  reviewed" and both posted.
- Request bodies are capped before being read into memory.

Correctness
- parse_diff_anchors counts a whitespace-stripped blank context line. Skipping
  it desynced the new-line counter for the rest of the hunk and silently
  misplaced every later inline comment in that file.
- post_inline_review's body-only fallback folds the anchored findings into the
  body. It previously posted a summary saying "N inline comment(s) below" with
  no comments and no findings — losing them all on the one path that matters.
- fetch_pr_diff's files-endpoint fallback emits real a// b/ prefixes (so
  changed_files and the anchor parser work on it) and reports both HTTP statuses
  in its error instead of the same one twice.
- The CI workflow template pins PRAGENT_ENGINE=ollama; review_pr defaults to
  opencode, which does not exist on a Gitea Actions runner.

Tests: 68 -> 101, covering each of the above.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01B11e8TZZxJyzHW7jj7KWUN
 2026-08-18 04:44:44 +00:00
Marcos 087834565d feat(pilot): token-usage reporting gated by AI-USAGE label 
Add per-review + per-comment token accounting, surfaced only when a PR carries
the new AI-USAGE label (on top of the existing AI-REVIEW trigger).

opencode_review:
- run_opencode now uses `--format json`; parse_opencode_events reconstructs the
  assistant text from `text` events and sums tokens/cost/steps from every
  `step_finish` event (tolerant of noise / missing fields).
- run() measures duration_s around the opencode call and returns (text, usage).
- changed_files(diff) extracts the `+++ b/` paths; the brief now lists them
  under a "Changed files" focus block so the agent grounds findings in the
  diff's neighbourhood instead of unbounded whole-repo walks.

ai_review:
- format_usage_section renders a `## AI usage` block: measured totals
  (in/out/reasoning/cache/cost/steps/duration), the whole-repo scope note, and
  an attributed per-finding table. Per-comment counts are output tokens split by
  each finding's body weight — labelled "attributed" since one model pass
  produces all findings.
- inline_comment_body appends `🪙 ~N tok (X% · attributed output)` when
  attribution is present.
- review_pr gains report_usage; compute_attribution stashes _tok_attrib/_tok_pct.
- format_review_body inserts the usage section between summary and findings.

webhook_server:
- Fire on every pull_request action except `closed` (denylist, was an allowlist)
  — the AI-REVIEW gate + sha dedupe keep this safe.
- AI-USAGE label detection + PRAGENT_USAGE_ALWAYS env drive report_usage.

.opencode factory + review-methodology skill: new "Ground findings in context"
step — read callers/imports/sibling functions per changed file (1-3 files per
finding), no unbounded walks.

Tests: parse_opencode_events (text+usage sum, malformed tolerance, none-usage),
changed_files, compute_attribution math, inline 🪙 line, format_usage_section
totals/table/cost, format_review_body ordering. 68 passing.

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-18 04:15:11 +00:00
Marcos 76b6752f48 fix(review): language-tagged suggestion fence for Gitea syntax highlighting 
Gitea 1.26.x has no GitHub-style 'Apply suggestion' button — a ```suggestion
fence is just an unknown-language code block, so chroma does not highlight it
and there is no apply control. Switch inline_comment_body to wrap the suggested
fix in a fence tagged with the file's language (new _lang_for_path helper,
.java→java, .ts→typescript, .py→python, ...), so Gitea syntax-highlights the
code. No capability lost (there was never an apply button on this Gitea
version). Correct the docstrings/skills/README that wrongly claimed an
apply-button was rendered.

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-18 01:26:37 +00:00
Marcos 6e3a9eb5b0 feat: opencode review engine + .opencode factory 
Replace the single Python model-call reviewer with an opencode agent
factory. A primary 'pragent' agent reads a brief (title/body/diff/config/
prior reviews), inspects the checked-out repo, runs the repo's own linters
via bash, loads review-methodology + findings-schema skills, and emits a
{summary, findings} JSON with per-finding severity/path/line/problem/fix/
suggestion/reference. Dormant security/tests/perf subagent lenses fan out
only on large/risky diffs (lean by default).

pilot/opencode_review.py: fetches the repo archive at the head sha into a
temp workdir, writes .pragent/brief.md, drops the factory, runs
'opencode run --pure --agent pragent --dir <workdir>' headlessly. Isolates
HOME (shared, warmed), strips ANTHROPIC_* env (leaked host vars caused
ProviderModelNotFoundError), stdin=DEVNULL (opencode blocks on stdin),
maps the bare OLLAMA_MODEL to the provider-prefixed ref. No Gitea I/O —
ai_review.review_pr parses + anchors + posts (reuses all v2 logic/tests).

PRAGENT_ENGINE=opencode (default) selects it; =ollama keeps the legacy
direct-call path. Verified end-to-end: posts a real review with a summary
section, inline [CRITICAL]/[HIGH] comments + apply-able suggestions +
reference links, and the sha dedupe marker. 49 tests pass.

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-08-17 23:01:59 +00:00