analyzing-debriefs

analyzing-debriefs is a skill for Claude Code from danielkinneyspears/federal-proposal-skills. It costs 88 tokens per session (1,579 once invoked), scanned A, original, Apache-2.0.

A process for requesting and interpreting a US federal procurement debrief, the government’s explanation of how it evaluated a bid after an award decision.

In plain words
What is it for?
Use it to prepare debrief questions, analyze the government’s explanation, perform win/loss analysis, and record lessons for future bids.
Why use it?
It turns feedback from a win or loss into specific lessons instead of relying on assumptions about what happened.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the federal-proposal-skills plugin — 22 skills shipped together

Good fit Use it to prepare debrief questions, analyze the government’s explanation, perform win/loss analysis, and record lessons for future bids.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add danielkinneyspears/federal-proposal-skills --skill analyzing-debriefs
Clone the repo
git clone --depth 1 https://github.com/danielkinneyspears/federal-proposal-skills

Made for: Claude Code.

Or install federal-proposal-skills, the plugin that ships this one along with the rest of its 22 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-debriefs

README.md
[![agentmods](https://agentmods.dev/badge/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs/github.svg)](https://agentmods.dev/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs)
Your own site
<a href="https://agentmods.dev/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs"><img src="https://agentmods.dev/badge/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analyzing-debriefs

Your own site · 80×15
<a href="https://agentmods.dev/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs"><img src="https://agentmods.dev/badge/skills/danielkinneyspears/federal-proposal-skills/analyzing-debriefs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 88 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,579 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00088 $0.01579
Opus 5 $0.00044 $0.00790
Sonnet 5 $0.00018 $0.00316
Haiku 4.5 $0.00009 $0.00158

Measured 11d ago against content hash 2b425d05244f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

analyzing-debriefs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyzing-debriefs/SKILL.md · 149 lines

How it starts

The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Debriefs

Close the loop on a pursuit. After award (win or lose) the government's debrief is the single best source of truth about how the proposal was actually evaluated. Most teams underuse it: they request it late, ask weak questions, or read a loss debrief through sour grapes. This skill helps request the debrief properly, interpret what the government said, and convert it into lessons that make the next bid better.

When to use this skill

Use this skill after a federal award decision, in two modes:

  • Before the debrief: to request it on time and prepare strong questions.
  • After the debrief: to analyze what the government said and capture win/loss lessons learned.

Use it for wins as well as losses. A win debrief reveals what worked and where the proposal was weaker than its score suggested — both valuable.

Inputs

Before the debrief:

  • The award notification and the solicitation (for the debrief rules).
  • The pursuit workspace artifacts — 01-bid-decision.md, 05-win-strategy.md, 20-color-review-*.md — so questions target what the team most needs to learn.

After the debrief:

  • The government's debrief content (the written debrief, slides, or notes from an oral debrief).
  • The same pursuit workspace artifacts, to compare what the team believed against what the government found.

Read ../../shared/glossary.md, ../../shared/federal-solicitation-primer.md, and ../../shared/pursuit-workspace.md if not already read this session.

Intake

Ask these as a numbered list.

  1. Mode. Is this preparing to request a debrief, or analyzing a debrief already received?
  2. Outcome. Won or lost? If lost, is the awardee known?
  3. Timing. When was the award notification received? (Debrief request windows are short and strict — see the method file.)
  4. Debrief type. Is the debrief written, oral, or a combination? What did the government provide or schedule?
  5. What the team wants to learn. What are the open questions — why the proposal scored as it did, how it compared, what the deciding factors were?

Read the full file on GitHub · 149 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 149 lines · 88 tokens per session scan A 2b425d05244f

Subscribe to this mod's changes

analyzing-debriefs is a skill published in the GitHub repository danielkinneyspears/federal-proposal-skills (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 88 tokens to every session and 1,579 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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